TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914248224931840 |
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| author | Tan, Zexi Xie, Tao Xiao, Haoyi Yang, Baoyao Ji, Yuzhu Zeng, An Zhang, Xiang Zhang, Yiqun |
| author_facet | Tan, Zexi Xie, Tao Xiao, Haoyi Yang, Baoyao Ji, Yuzhu Zeng, An Zhang, Xiang Zhang, Yiqun |
| contents | Multivariate Time-Series (MTS) clustering is crucial for signal processing and data analysis. Although deep learning approaches, particularly those leveraging Contrastive Learning (CL), are prominent for MTS representation, existing CL-based models face two key limitations: 1) neglecting clustering information during positive/negative sample pair construction, and 2) introducing unreasonable inductive biases, e.g., destroying time dependence and periodicity through augmentation strategies, compromising representation quality. This paper, therefore, proposes a Temporal-Frequency Enhanced Contrastive (TFEC) learning framework. To preserve temporal structure while generating low-distortion representations, a temporal-frequency Co-EnHancement (CoEH) mechanism is introduced. Accordingly, a synergistic dual-path representation and cluster distribution learning framework is designed to jointly optimize cluster structure and representation fidelity. Experiments on six real-world benchmark datasets demonstrate TFEC's superiority, achieving 4.48% average NMI gains over SOTA methods, with ablation studies validating the design. The code of the paper is available at: https://github.com/yueliangy/TFEC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07550 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning Tan, Zexi Xie, Tao Xiao, Haoyi Yang, Baoyao Ji, Yuzhu Zeng, An Zhang, Xiang Zhang, Yiqun Machine Learning Multivariate Time-Series (MTS) clustering is crucial for signal processing and data analysis. Although deep learning approaches, particularly those leveraging Contrastive Learning (CL), are prominent for MTS representation, existing CL-based models face two key limitations: 1) neglecting clustering information during positive/negative sample pair construction, and 2) introducing unreasonable inductive biases, e.g., destroying time dependence and periodicity through augmentation strategies, compromising representation quality. This paper, therefore, proposes a Temporal-Frequency Enhanced Contrastive (TFEC) learning framework. To preserve temporal structure while generating low-distortion representations, a temporal-frequency Co-EnHancement (CoEH) mechanism is introduced. Accordingly, a synergistic dual-path representation and cluster distribution learning framework is designed to jointly optimize cluster structure and representation fidelity. Experiments on six real-world benchmark datasets demonstrate TFEC's superiority, achieving 4.48% average NMI gains over SOTA methods, with ablation studies validating the design. The code of the paper is available at: https://github.com/yueliangy/TFEC. |
| title | TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.07550 |